

Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes



Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say

Andre Exner

"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."

Chase Zieman

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
Begin by setting up a Python environment and ensuring you have access to tools like `pip` and `setuptools`, which are necessary for interacting with PyPI. Additionally, set up an Apache Iceberg environment. You might use a local Iceberg installation or a cloud-based setup, depending on your requirements. Ensure you have Hadoop or a compatible file system for Iceberg storage.
Use Python scripts to collect data from PyPI. You can utilize the PyPI JSON API to fetch package metadata. For example, you can use `requests` library in Python to get package information:
```python
import requests
package_name = "example-package"
url = f"https://pypi.org/pypi/{package_name}/json"
response = requests.get(url)
package_data = response.json()
```
Extract relevant metadata, such as version, author, and release date, which you plan to move to Iceberg.
Convert the collected data into a format suitable for Iceberg. Apache Iceberg supports Parquet, Avro, and ORC file formats. Use Python libraries like `pandas` and `pyarrow` to transform JSON data into Parquet or another supported format:
```python
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
df = pd.DataFrame.from_records(package_data['releases'])
table = pa.Table.from_pandas(df)
pq.write_table(table, 'pypi_data.parquet')
```
Ensure the schema of your data aligns with the Iceberg table schema you plan to use.
Set up the Iceberg table to receive the data. Define the schema and partitioning strategy that suits your data access patterns. This is typically done using SQL with a compatible engine like Apache Spark or Presto. For example:
```sql
CREATE TABLE pypi_data (
version STRING,
author STRING,
release_date DATE
) USING iceberg
PARTITIONED BY (release_date);
```
Move the transformed data files (Parquet, Avro, or ORC) into the location where your Iceberg table expects data. This could involve copying files into a specific directory in your Hadoop or cloud storage setup. Ensure the file location and naming conventions match those expected by Iceberg.
Use SQL queries to ingest the data files into the Iceberg table. This might involve using a tool like Apache Hive, Apache Spark, or any compatible SQL engine:
```sql
INSERT INTO pypi_data
SELECT * FROM parquet_file('path/to/pypi_data.parquet');
```
This step ensures that the data is correctly indexed and stored in Iceberg's structured format.
Finally, verify the integrity and correctness of the data loaded into Iceberg. Run queries to check counts, validate schema conformity, and ensure data accuracy:
```sql
SELECT COUNT(*) FROM pypi_data;
SELECT * FROM pypi_data WHERE version = '1.0.0';
```
Ensure that the data aligns with what was extracted from PyPI and that no data loss or corruption occurred during the transfer process. Adjustments can be made as necessary based on these checks.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
The Python Package Index (PyPI) is a storehouse of software for the Python programming language. The Python Package Index abbreviated as PyPI and also non as the Cheese Shop is the official third-party software repository for Python. PyPI assists the users to search and install software that has been developed and shared by the Python community. PyPI, typically pronounced pie-pee-eye, is a repository containing several hundred thousand packages. The ability to provision PyPI packages from Artifact to the pip command line tool from all repository types.
PyPI's API provides access to a wide range of data related to Python packages and their metadata. The following are the categories of data that can be accessed through PyPI's API:
1. Package information: This includes data related to the package name, version, description, author, license, and other metadata.
2. Release information: This includes data related to the release date, download URL, and other information about each release of a package.
3. Project information: This includes data related to the project's homepage, bug tracker, and other project-related information.
4. User information: This includes data related to the user's account, such as their username, email address, and other profile information.
5. Search results: This includes data related to the search results for a particular query, including package names, descriptions, and other metadata.
6. Download statistics: This includes data related to the number of downloads for a particular package or release.
Overall, PyPI's API provides a comprehensive set of data related to Python packages and their metadata, making it a valuable resource for developers and researchers.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: